Performing radio frequency matching control using a model-based digital twin

ABSTRACT

A method includes causing manufacturing equipment to generate a RF signal to energize a processing chamber associated with the manufacturing equipment. The method further includes receiving, from one or more sensors associated with the manufacturing equipment, current trace data associated with the RF signal. The method further includes updating impedance values of a digital replica associated with the manufacturing equipment based on the current trace data. The method further includes obtaining, from the digital replica, one or more outputs indicative of predictive data. The method further includes causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.

TECHNICAL FIELD

The present disclosure relates to electrical components, and, moreparticularly, to performing radio frequency matching control using amodel-based digital twin.

BACKGROUND

Products may be produced by performing one or more manufacturingprocesses using manufacturing equipment. For example, semiconductormanufacturing equipment may be used to produce semiconductor devices(e.g., substrates, wafers, etc.) via semiconductor manufacturingprocesses. In the conventional radio frequency (RF) plasma etchingprocess used during stages of fabrication of many semiconductor devices,an RF signal may be provided to a substrate processing chamber via an RFenergy source. The RF signal may be generated and provided in continuousor pulsed wave modes. Due to mismatches between the impedance of the RFenergy source and the plasma formed in the processing chamber, some ofthe RF signal is reflected back to the RF energy source, which resultsin inefficient use of the RF signal and wasting energy, potential damageto RF energy source, and potential inconsistency/non-repeatabilityissues with respect to substrate processing. Thus, a RF matching controlsystem is capable of achieving high repeatability of delivering RFsignal is desirable.

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate any scope of the particular implementations of the disclosureor any scope of the claims. Its sole purpose is to present some conceptsof the disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

In an aspect of the disclosure, a method includes causing manufacturingequipment to generate a RF signal to energize a processing chamberassociated with the manufacturing equipment. The method further includesreceiving, from one or more sensors associated with the manufacturingequipment, current trace data associated the RF signal. The methodfurther includes updating impedance values of a digital replicaassociated with the manufacturing equipment based on the current tracedata. The method further includes obtaining, from the digital replica,one or more outputs indicative of predictive data. The method furtherincludes causing, based on the predictive data, performance of one ormore corrective actions associated with the manufacturing equipment.

In another aspect of the disclosure, a system includes a memory; and aprocessing device, coupled to the memory, to cause manufacturingequipment to generate a RF signal to energize a processing chamberassociated with the manufacturing equipment. The processing device isfurther to receive, from one or more sensors associated with themanufacturing equipment, current trace data associated the RF signal.The processing device is further to update impedance values of a digitalreplica associated with the manufacturing equipment based on the currenttrace data. The processing device is further to obtain, from the digitalreplica, one or more outputs indicative of predictive data. Theprocessing device is further to cause, based on the predictive data,performance of one or more corrective actions associated with themanufacturing equipment

In another aspect of the disclosure, a non-transitory machine-readablestorage medium storing instructions which, when executed cause aprocessing device to perform operations including causing manufacturingequipment to generate a RF signal to energize a processing chamberassociated with the manufacturing equipment. The operations furtherinclude receiving, from one or more sensors associated with themanufacturing equipment, current trace data associated the RF signal.The operations further include updating impedance values of a digitalreplica associated with the manufacturing equipment based on the currenttrace data. The operations further include obtaining, from the digitalreplica, one or more outputs indicative of predictive data. Theoperations further include causing, based on the predictive data,performance of one or more corrective actions associated with themanufacturing equipment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating an exemplary system architecture,according to certain embodiments.

FIG. 2 is a block diagram illustrating an exemplary embodiment of themanufacturing equipment in greater detail, according to certainembodiments.

FIG. 3 is a flow diagram for with generating predictive data to cause acorrective action, according to certain embodiments.

FIGS. 4A-C are graphs illustrating example optimization profiles,according to certain embodiments.

FIG. 5 is a graph illustrating multiple tuning paths associated twovariable capacitors, according to certain embodiments.

FIG. 6 is a block diagram illustrating a computer system, according tocertain embodiments.

DETAILED DESCRIPTION

Described herein are technologies directed to performing radio frequencymatching control using a model-based digital twin. Manufacturingequipment may be used to produce products (e.g., wafers). For example,manufacturing equipment may execute a recipe to produce wafers bysuppling RF energy, in the form of an RF signal, to a processing chamberto generate plasma for etching. The RF signal is often coupled to theplasma in the processing chamber through a fixed or tunable matchingnetwork that operates to minimize the reflected RF signal by moreclosely matching the impedance of the plasma to the impedance of the RFenergy source. The matching network allows the output of the RF sourceto be efficiently coupled to the plasma to maximize the amount of energycoupled to the plasma. Thus, the matching network allows for the totalimpedance (e.g., plasma impedance, chamber impedance, and matchingnetwork impedance) to be the same as or similar to the output impedanceof the RF power delivery.

In some conventional systems, RF match control profiles for the matchingnetwork are designed based on lookup tables. The lookup tables may beassociated with processing parameters, such as the type of inputfrequency (e.g., 2.2 MHz, 13.56 MHz, etc.) used energize the processingchamber, the type of processing gas used (e.g., H₂, He, Ar, O₂, NF₃,etc.), the type of processing chamber used, etc. The control profilescan be used to adjust the one or more variable tuning elements of thematching network, such as a variable capacitor, variable inductor, orvariable resistor. When the parameters of the matching network orprocessing chamber change (due to temperature, aging effects, corrosion,parts failure, degradation, etc.), the control profile selected usingthe lookup tables may not deliver optimal results.

The devices, systems, and methods disclosed herein provide real-timeoptimization of a control profile using current sensor data to achievemaximum power delivery with minimum tuning time. In one embodiment, aprocessing device causes manufacturing equipment to generate a RF signalto energize a processing chamber associated with the manufacturingequipment. The processing device may receive, from one or more sensorsassociated with the manufacturing equipment, current trace data (e.g.,voltage, current, etc.) associated the RF signal. The processing devicemay then update impedance values of a digital replica associated withthe manufacturing equipment based on the current trace data. Outputsindicative of predictive data may be obtained from the digital replica.The outputs indicative of predictive data may be generated using atrained machine learning model, a heuristics model, or a rule basedmodel. Based on the predictive data, the processing device may performone or more corrective actions associated with the manufacturingequipment.

In some embodiments, the matching network may include two adjustable(tunable) capacitors. The predictive data may include one or more tunesettings for the capacitors. The tune settings may be a function of thereflective coefficient associated with the reflected RF signal and atime parameter. The corrective action may include adjusting either orboth of the adjustable capacitors based on the predictive data.

In some embodiments, the processing device can update the digitalreplica in real-time using the current trace data. For example,responsive to determining that digital replica fails to satisfy anaccuracy threshold criterion based on the current trace data, theprocessing logic can perform an optimization of the digital replica.

In some embodiments, the matching network may include an input impedancesensor and an output impedance sensor. Updating the impedance values ofthe digital replica may include updating a processing chamber modelassociated with the digital replica using the current trace data fromthe output impedance sensor. Updating the impedance values of thedigital replica may further include updating a matching network modelassociated with the digital replica using current trace data from theinput impedance sensor and the output impedance sensor.

Aspects of the present disclosure result in technological advantages ofsignificant reduction in time required to achieve optimal settings,energy consumption, bandwidth used, processor overhead, and so forth.For example, conventional systems perform iterations of trial and errorto attempt to improve high repeatability in delivering optimal RF power.This trial-and-error process requires more and more time, energyconsumption, bandwidth, and processor overhead for each iteration (e.g.,generating instructions via trial and error, transmitting theinstructions, receiving feedback, generating updated instructions viatrial and error, and so forth). The present disclosure results inreduced time requirements, energy consumption, bandwidth, and processoroverhead by using signal processing, a digital replica, and a machinelearning model to obtain predictive data and cause performance ofcorrective actions based on the predictive data and avoiding iterationsof trial and error. The present disclosure may result in predictingoptimal parameter settings associated with the matching network to avoidinconsistent and abnormal products, unscheduled user time, and damage tomanufacturing equipment.

FIG. 1 is a block diagram illustrating an exemplary system 100(exemplary system architecture), according to certain embodiments. Thesystem 100 includes a simulation system 110, a client device 120,sensors 126, manufacturing equipment 130, metrology equipment 128, and adata store 140. The simulation system 110 may include a digitalrepresentation server 170, a server machine 180, and a predictive server112.

The manufacturing equipment 130 can include RF generator 132, matchingnetwork 134, process chamber 136, and controller 138. A RF signal may begenerated by the RF generator 132 and transmitted to the matchingnetwork 134. The RF signal may then be applied to the processing chamber136 to ignite and maintain plasma used for an etching process. In someembodiments, the RF generator 132 may generate one or more of a low RFsignal (e.g., 2.2 MHz, 13.56 MHz, etc.) to energize the processingchamber 136, one or more of a high RF signal (e.g., 24 MHz, 60 MHz, 100MHz, etc.) to energize the processing chamber 136, or any combinationthereof. The RF generator 132 may have capabilities of pulsing the RFsignal at a desired pulse rate, duty cycle, and phase angle. Thecontroller 138 may be connected to the RF power generator 132 and RFmatching network 134, and may control (e.g., initiate, switch, shut off,etc.) the RF signal of the RF generator 132. Further, the controller 138may be used to adjust the pulse rate, duty cycle, and phase angle of theRF signal.

The matching network 134 may operate to minimize reflected RF energy bymatching the impedance of the plasma used in the etching process to theimpedance of the RF signal (e.g., 50 Ohms) supplied by the RF generator132. The matching network 134 may include an arrangement of capacitive,inductive, and resistive elements. In order to vary its parameters, thematching network 134 may include one or more controllable adjustable orvariable tuning elements, such as a variable capacitor, inductor, orresistor, or a combination thereof. For example, the matching network134 can include a variable shunt capacitor and a variable seriescapacitor, both of which function as the variable tuning element. Thevariable (e.g., tuning or tunable) capacitor may be a motorized vacuumcapacitor operated by the controller 138, or any other controllablevariable capacitor. In an embodiment, the matching network 134 may tuneone or more of the controllable variable tuning elements such that theimpedance associated with the matching network 134 and/or the processingchamber 136 is increased/decreased towards the impedance associated withthe RF generator 132 (e.g., 50 Ohms).

The RF matching network 134 may also include one or more electricalsensors 135, which may be any type of RF voltage/current measurementdevice (e.g., a sensor, a probe, etc.) capable of providing sensor data142 (e.g., sensor values, features, trace data). In some embodiments,the electrical sensors 135 perform electrical measurements of anelectrical feed conductor (e.g., electrical feed lines) coupled to theRF generator 132, the matching network 134, and/or the process chamber136. The electrical sensors 135 sense properties of the electrical feedconductor (e.g., impedance, magnetic fluctuations in the electrical feedconductor, electrical current, voltage, resistance, etc.) and convertthe properties into sensor data 142 (e.g., trace data, historical tracedata 146, current trace data 154). The electrical sensors 135 may alsomeasure values of one or more of electrical current, magnitude ofAlternating Current (AC), phase, waveform (e.g., AC waveform, pulsewaveform), Direct Current (DC), non-sinusoidal AC waveforms, voltage, orthe like.

In an embodiment, the electrical sensors 135 include an input (source)impedance sensor and output (load) impedance sensor. The input impedancesensor may determine the impedance associated with the RF signal at thematching network 134. The output impedance sensor may determine theimpedance of the RF signal at a destination (e.g., the processingchamber 136). The manufacturing equipment 130 will be described ingreater detail in regards to FIG. 2.

The sensors 126 may provide sensor data 142 (e.g., sensor values,features, trace data) associated with manufacturing equipment 130 (e.g.,associated with producing, by manufacturing equipment 130, correspondingproducts, such as wafers). The sensor data 142 may be used for equipmenthealth and/or product health (e.g., product quality). The manufacturingequipment 130 may produce products following a recipe or performing runsover a period of time. Sensor data 142 received over a period of time(e.g., corresponding to at least part of a recipe or run) may bereferred to as trace data (e.g., historical trace data 146, currenttrace data 154) received from different sensors 126 over time.

The sensors 126 may include additional sensors that provide other typesof sensor data 142. In some embodiments, the sensor data 142 may includevalues of one or more of temperature (e.g., heater temperature), spacing(SP), pressure, High Frequency Radio Frequency (HFRF), voltage ofElectrostatic Chuck (ESC), electrical current, flow, power, voltage,etc. Sensor data 142 may be associated with or indicative ofmanufacturing parameters such as hardware parameters (e.g., settings orcomponents (e.g., size, type, etc.) of the manufacturing equipment 130)or process parameters of the manufacturing equipment. The sensor data142 may be provided while the manufacturing equipment 130 is performingmanufacturing processes (e.g., equipment readings when processingproducts). The sensor data 142 may be different for each product (e.g.,each wafer).

In some embodiments, the sensor data 142 (e.g., historical trace data146, sets of historical component data 148, current trace data 154, setsof current component data 156, etc.) may be processed (e.g., by theclient device 120 and/or by components of the simulation system 110).Processing of the sensor data 142 may include generating features. Insome embodiments, the features are a pattern in the sensor data 142(e.g., slope, width, height, peak, etc.) or a combination of sensorvalues from the sensor data 142 (e.g., impedance derived from voltageand current measurements, etc.). The sensor data 142 may includefeatures and the features may be used by components of the simulationsystem 110 for performing simulation processing and/or for obtainingsimulation data 167 and/or predictive data 168 for performance of acorrective action.

Data store 140 may be a memory (e.g., random access memory), a drive(e.g., a hard drive, a flash drive), a database system, or another typeof component or device capable of storing data. Data store 140 mayinclude multiple storage components (e.g., multiple drives or multipledatabases) that may span multiple computing devices (e.g., multipleserver computers). The data store 140 may store sensor data 142,performance data 160, library data 166, simulation data 168, andpredictive data 169. Sensor data 142 may include historical sensor data144 and current sensor data 152. Historical sensor data may includehistorical trace data 146, sets of historical component data 148, andhistorical component identifiers 150. Current sensor data 152 mayinclude current trace data 154, sets of current component data 156, andcurrent component identifiers 158. Performance data 160 may includehistorical performance data 162 and current performance data 164. Thehistorical sensor data 144 and historical performance data 162 may behistorical data. The current sensor data 144 may be current data forwhich simulation data 167 and predictive data 168 are to be generated(e.g., for performing corrective actions). The current performance data164 may also be current data (e.g., for re-training trained machinelearning model)

The performance data 160 may include data associated with themanufacturing equipment 130 and/or products produced by themanufacturing equipment 130. In some embodiments, the performance data160 may include an indication of a lifetime of a component ofmanufacturing equipment 130 (e.g., time of failure), manufacturingparameters of manufacturing equipment 130, maintenance of manufacturingequipment 130, energy usage of a component of manufacturing equipment130, variance in components (e.g., of same part number) of manufacturingequipment 130, or the like. Performance data 160 may include anindication of variance in components (e.g., of the same type, of thesame part number) of manufacturing equipment. The performance data 160may indicate if the variance (e.g., jitter, slope, peak, etc.)contributes to product-to-product variation. The performance data 160may indicate if a variance provides an improved wafer (e.g., RFgenerator better matched, feedback loop tuned better, newer firmware,better chips). The performance data 160 may be associated with a qualityof products produced by the manufacturing equipment 130. The metrologyequipment 128 may provide performance data 160 (e.g., property data ofwafers, yield, metrology data) associated with products (e.g., wafers)produced by the manufacturing equipment 130. The performance data 160may include a value of one or more of film property data (e.g., waferspatial film properties), dimensions (e.g., thickness, height, etc.),dielectric constant, dopant concentration, density, defects, etc. Theperformance data 160 may be of a finished or semi-finished product. Theperformance data 160 may be different for each product (e.g., eachwafer). The performance data 160 may indicate whether a product meets athreshold quality (e.g., defective, not defective, etc.). Theperformance data 160 may indicate a cause of not meeting a thresholdquality. In some embodiments, the performance data 160 includeshistorical performance data 162, which corresponds to historicalproperty data of products (e.g., produced using manufacturing parametersassociated with historical trace data 146). The sensor data 142,performance data 160, and library data 166 may be used for supervisedand/or unsupervised machine learning.

The simulation system 110 may include digital representation server 170,server machine 180, and predictive server 112. The predictive server112, digital representation server 170, and server machine 180 may eachinclude one or more computing devices such as a rackmount server, arouter computer, a server computer, a personal computer, a mainframecomputer, a laptop computer, a tablet computer, a desktop computer,Graphics Processing Unit (GPU), accelerator Application-SpecificIntegrated Circuit (ASIC) (e.g., Tensor Processing Unit (TPU)), etc.

The digital representation server 170 may be an algorithmic model thatsimulates manufacturing equipment 130. By way of example, the digitalrepresentation server 170 may be a digital replica model (e.g., adigital twin) of the manufacturing equipment 130. The digitalrepresentation server 170 may use supervised machine learning,semi-supervised learning, unsupervised machine learning, or anycombination thereof to generate a virtual representation of the physicalelements and the dynamics of how the manufacturing equipment 130operates. The digital representation server 170 may be updated viareinforcement learning using periodic updates from the sensors 126,electrical sensors 135, sensor data 142, performance data 160, librarydata 166, and/or any other data associated with generating and maintainthe digital replica model of the manufacturing equipment 130.

The digital representation server 170 may include a matching networkmodel 172 and a processing chamber model 174. The matching network model172 may be associated with the physical elements and the dynamics of thematching network 134. The processing chamber model 174 may be associatedwith the physical elements and the dynamics of the processing chamber136.

In some embodiments, the digital representation server 170 may generatesimulation data 167. Simulation data 167 may include data used todetermine how the manufacturing equipment 130 (e.g., matching network134, processing chamber 136, etc.) would perform based on current orsimulated parameters. The simulation data 167 may include electricalparameter data (e.g., impedance, voltage, current, resistance,reflective, signal reflection, etc.) associated with the matchingnetwork model 172 and the processing chamber model 174. The simulationdata 167 may further include predicted property data of the digitalreplica model of the manufacturing equipment 130 (e.g., of products tobe produced or that have been produced using the current trace data154). The simulation data 167 may further include predicted metrologydata (e.g., virtual metrology data) of the products to be produced orthat have been produced using the current trace data 154. The simulationdata 167 may further include an indication of abnormalities (e.g.,abnormal products, abnormal components, abnormal manufacturing equipment130, abnormal energy usage, etc.) and one or more causes of theabnormalities. The simulation data 167 may further include an indicationof an end of life of a component of manufacturing equipment 130. Thesimulation data may be all encompassing, covering every mechanical andelectrical aspect of the manufacturing equipment.

The predictive server 112 may include a predictive component 114. Insome embodiments, the predictive component 114 may receive simulationdata 167 and current trace data 154 (e.g., processing chamber flow,processing chamber pressure, RF power, etc.) and generate output (e.g.,predictive data 168) for performing corrective action associated withthe manufacturing equipment 130. In some embodiments, the predictivecomponent 114 may use one or more trained machine learning models 190 todetermine the output for performing the corrective action based on thesimulation data 167 and current trace data 154. In some embodiments, thepredictive component 114 receives simulation data 167 and current tracedata 154, performs signal processing to break down the current tracedata 154 into sets of current component data 156 mapped to currentcomponent identifiers 158, provides the sets of current component data156 and the current component identifiers 158 as input to a trainedmachine learning model 190, and obtains outputs indicative of predictivedata 168 from the trained machine learning model 190. The trainedmachine learning model 190 may include a single model, or multiplemodels. In some embodiments, the trained machine learning model 190 mayuse additional data from the data store 140 (e.g., library data 166,performance data 160, sensor data 142, etc.)

In some embodiments, simulation system 110 further includes servermachine 180. Server machine 180 may, using a data set generator,generate one or more data sets (e.g., a set of data inputs and a set oftarget outputs) to train, validate, and/or test a machine learningmodel(s) 190. In particular, server machine 180 can include a trainingengine 182, a validation engine 184, selection engine 185, and/or atesting engine 186. An engine (e.g., training engine 182, a validationengine 184, selection engine 185, and a testing engine 186) may refer tohardware (e.g., circuitry, dedicated logic, programmable logic,microcode, processing device, etc.), software (such as instructions runon a processing device, a general purpose computer system, or adedicated machine), firmware, microcode, or a combination thereof. Thetraining engine 182 may be capable of training one or more machinelearning model 190 using one or more sets of features associated withthe training set from data set generator 172. The training engine 182may generate multiple trained machine learning models 190, where eachtrained machine learning model 190 corresponds to a distinct set offeatures of the training set (e.g., sensor data from a distinct set ofsensors). For example, a first trained machine learning model may havebeen trained using all features (e.g., X1-X5), a second trained machinelearning model may have been trained using a first subset of thefeatures (e.g., X1, X2, X4), and a third trained machine learning modelmay have been trained using a second subset of the features (e.g., X1,X3, X4, and X5) that may partially overlap the first subset of features.

The validation engine 184 may be capable of validating a trained machinelearning model 190 using a corresponding set of features of thevalidation set from data set generator. For example, a first trainedmachine learning model 190 that was trained using a first set offeatures of the training set may be validated using the first set offeatures of the validation set. The validation engine 184 may determinean accuracy of each of the trained machine learning models 190 based onthe corresponding sets of features of the validation set. The validationengine 184 may discard trained machine learning models 190 that have anaccuracy that does not meet a threshold accuracy. In some embodiments,the selection engine 185 may be capable of selecting one or more trainedmachine learning models 190 that have an accuracy that meets a thresholdaccuracy. In some embodiments, the selection engine 185 may be capableof selecting the trained machine learning model 190 that has the highestaccuracy of the trained machine learning models 190.

The testing engine 186 may be capable of testing a trained machinelearning model 190 using a corresponding set of features of a testingset from data set generator. For example, a first trained machinelearning model 190 that was trained using a first set of features of thetraining set may be tested using the first set of features of thetesting set. The testing engine 186 may determine a trained machinelearning model 190 that has the highest accuracy of all of the trainedmachine learning models based on the testing sets.

The machine learning model 190 may refer to the model artifact that iscreated by the training engine 182 using a training set that includesdata inputs and corresponding target outputs (correct answers forrespective training inputs). Patterns in the data sets can be found thatmap the data input to the target output (the correct answer), and themachine learning model 190 is provided mappings that captures thesepatterns. The machine learning model 190 may use one or more of SupportVector Machine (SVM), Radial Basis Function (RBF), clustering,supervised machine learning, semi-supervised machine learning,unsupervised machine learning, k-Nearest Neighbor algorithm (k-NN),linear regression, random forest, neural network (e.g., artificialneural network), etc.

Predictive component 114 may provide simulation data 167 and currentsensor data 152 to the trained machine learning model 190 and may runthe trained machine learning model 190 on the input to obtain one ormore outputs. The predictive component 114 may be capable of determining(e.g., extracting) predictive data 168 from the output of the trainedmachine learning model 190

For purpose of illustration, rather than limitation, aspects of thedisclosure describe the training of one or more machine learning models190 and inputting simulation data 167 and sensor data 142 into the oneor more trained machine learning models 190 to determine predictive data168. In other implementations, a heuristic model or rule-based model isused to determine predictive data 168 (e.g., without using a trainedmachine learning model). Predictive component 114 may monitor historicalsensor data 144 and historical performance data 162. Any of theinformation described with respect to data from the data store 140 maybe monitored or otherwise used in the heuristic or rule-based model.

In some embodiments, the functions of client device 120, predictiveserver 112, digital representation server 170, and server machine 180may be provided by a fewer number of machines. For example, in someembodiments, digital representation server 170 and server machine 180may be integrated into a single machine, while in some otherembodiments, digital representation server 170 and server machine 180,and predictive server 112 may be integrated into a single machine. Insome embodiments, client device 120 and predictive server 112 may beintegrated into a single machine.

In general, functions described in one embodiment as being performed byclient device 120, predictive server 112, digital representation server170 and server machine 180 can also be performed on predictive server112 in other embodiments, if appropriate. In addition, the functionalityattributed to a particular component can be performed by different ormultiple components operating together. For example, in someembodiments, the predictive server 112 may determine the correctiveaction based on the predictive data 168. In another example, clientdevice 120 may determine the predictive data 168 based on output fromthe trained machine learning model.

In addition, the functions of a particular component can be performed bydifferent or multiple components operating together. One or more of thepredictive server 112, digital representation server 170 and servermachine 180 may be accessed as a service provided to other systems ordevices through appropriate application programming interfaces (API).

In embodiments, a “user” may be represented as a single individual.However, other embodiments of the disclosure encompass a “user” being anentity controlled by a plurality of users and/or an automated source.For example, a set of individual users federated as a group ofadministrators may be considered a “user.”

Embodiments of the disclosure may be applied to data quality evaluation,feature enhancement, model evaluation, Virtual Metrology (VM),Predictive Maintenance (PdM), limit optimization, or the like.

Although embodiments of the disclosure are discussed in terms ofgenerating predictive data 168 to perform a corrective action inmanufacturing facilities (e.g., semiconductor manufacturing facilities),embodiments may also be generally applied to characterizing andmonitoring components. Embodiments may be generally applied tocharacterizing and monitoring based on different types of data.

The client device 120, manufacturing equipment 130, sensors 126,metrology equipment 128, predictive server 112, data store 140, digitalrepresentation server 170, and server machine 180 may be coupled to eachother via a network 105 for generating predictive data 168 to performcorrective actions.

In some embodiments, network 105 is a public network that providesclient device 120 with access to the predictive server 112, data store140, and other publically available computing devices. In someembodiments, network 105 is a private network that provides clientdevice 120 access to manufacturing equipment 130, sensors 126, metrologyequipment 128, data store 140, and other privately available computingdevices. Network 105 may include one or more Wide Area Networks (WANs),Local Area Networks (LANs), wired networks (e.g., Ethernet network),wireless networks (e.g., an 802.11 network or a Wi-Fi network), cellularnetworks (e.g., a Long Term Evolution (LTE) network), routers, hubs,switches, server computers, cloud computing networks, and/or acombination thereof.

The client device 120 may include a computing device such as PersonalComputers (PCs), laptops, mobile phones, smart phones, tablet computers,netbook computers, network connected televisions (“smart TV”),network-connected media players (e.g., Blu-ray player), a set-top-box,Over-the-Top (OTT) streaming devices, operator boxes, etc. The clientdevice 120 may include a corrective action component 122. Correctiveaction component 122 may receive user input (e.g., via a Graphical UserInterface (GUI) displayed via the client device 120) of an indicationassociated with manufacturing equipment 130. In some embodiments, thecorrective action component 122 transmits the indication to thesimulation system 110, receives output (e.g., predictive data 168) fromthe simulation system 110, determines a corrective action based on theoutput, and causes the corrective action to be implemented. In someembodiments, the corrective action component 122 obtains sensor data 142(e.g., current trace data 154) associated with the manufacturingequipment 130 (e.g., from data store 140, etc.) and provides the sensordata 142 (e.g., current trace data 154) associated with themanufacturing equipment 130 to the simulation system 110. In someembodiments, the corrective action component 122 stores sensor data 142in the data store 140 and the predictive server 112 retrieves the sensordata 142 from the data store 140. In some embodiments, the predictiveserver 112 may store output (e.g., predictive data 168) of the trainedmachine learning model(s) 190 in the data store 140 and the clientdevice 120 may retrieve the output from the data store 140. In someembodiments, the corrective action component 122 receives an indicationof a corrective action from the simulation system 110 and causes thecorrective action to be implemented. Each client device 120 may includean operating system that allows users to one or more of generate, view,or edit data (e.g., indication associated with manufacturing equipment130, corrective actions associated with manufacturing equipment 130,etc.).

Performing manufacturing processes that result in defective products canbe costly in time, energy, products, components, manufacturing equipment130, the cost of identifying the defects and discarding the defectiveproduct, etc. By inputting sensor data 142 (e.g., manufacturingparameters that are being used or are to be used to manufacture aproduct), receiving output of predictive data 168, and performing acorrective action based on the predictive data 168, system 100 can havethe technical advantage of avoiding the cost of producing, identifying,and discarding defective products.

Performing manufacturing processes that result in failure of thecomponents of the manufacturing equipment 124 can be costly in downtime,damage to products, damage to equipment, express ordering replacementcomponents, etc. By inputting sensor data 142 (e.g., manufacturingparameters that are being used or are to be used to manufacture aproduct), receiving output of predictive data 168, and performingcorrective action (e.g., predicted operational maintenance, such asreplacement, processing, cleaning, etc. of components) based on thepredictive data 168, system 100 can have the technical advantage ofavoiding the cost of one or more of unexpected component failure,unscheduled downtime, productivity loss, unexpected equipment failure,product scrap, or the like.

Corrective action may be associated with one or more of ComputationalProcess Control (CPC), Statistical Process Control (SPC) (e.g., SPC onelectronic components to determine process in control, SPC to predictuseful lifespan of components, SPC to compare to a graph of 3-sigma,etc.), Advanced Process Control (APC), model-based process control,preventative operative maintenance, design optimization, updating ofmanufacturing parameters, feedback control, machine learningmodification, or the like. In some embodiments, the corrective actionmay include adjusting one or more of the controllable variable tuningelements of the matching network 134. This will be explained in furtherdetail below.

In some embodiments, the corrective action includes providing an alert(e.g., an alarm to stop or not perform the manufacturing process if thepredictive data 168 indicates a predicted abnormality, such as anabnormality of the product, a component, or manufacturing equipment130). In some embodiments, the corrective action includes providingfeedback control (e.g., modifying a manufacturing parameter responsiveto the predictive data 168 indicating a predicted abnormality). In someembodiments, the corrective action includes providing machine learning(e.g., modifying one or more manufacturing parameters based on thepredictive data 168). In some embodiments, performance of the correctiveaction includes causing updates to one or more manufacturing parameters.

Manufacturing parameters may include hardware parameters (e.g.,replacing components, using certain components, replacing a processingchip, updating firmware, etc.) and/or process parameters (e.g.,temperature, pressure, flow, rate, electrical current, voltage, gasflow, lift speed, etc.). In some embodiments, the corrective actionincludes causing preventative operative maintenance (e.g., replace,process, clean, etc. components of the manufacturing equipment 130). Insome embodiments, the corrective action includes causing designoptimization (e.g., updating manufacturing parameters, manufacturingprocesses, manufacturing equipment 130, etc. for an optimized product).

In some embodiments, the corrective action includes updating a recipe(e.g., manufacturing equipment 130 to be in an idle mode, a sleep mode,a warm-up mode, etc.).

FIG. 2 is a block diagram illustrating an exemplary embodiment of themanufacturing equipment 130 in greater detail. The components of FIG. 2are used by way of example for illustrative purposes. Accordingly,different combinations of components, such as insulators, capacitors,resistors, etc., may be used with the embodiments of the presentdisclosure. As discussed above, the manufacturing equipment 130 includesthe RF generator 132, the matching network 134, the processing chamber136, and the controller 138.

The matching network 126 may include a first capacitor 226 (alsoreferred to as “C1”) and second capacitor 228 (also referred to as“C2”). Each of the first capacitor 226 and the second capacitor 228 canbe a variable capacitor (tuning or adjustable capacitor) capable oftuning the total impedance of the matching network 134. For example,embodiments of the present disclosure may perform a match tuning byadjusting either or both of the first capacitor 226 and the secondcapacitor 228 such that the output impedance associated with theprocessing chamber 136 is adjusted towards the input impedanceassociated with the RF generator 132 (e.g., 50 ohms). This can maximizethe power delivery from the RF generator 132 to the processing chamber136. In some embodiments, the first capacitor 226 may further be a shuntcapacitor while the second capacitor 228 may be a series capacitor.Series capacitors can be used in transmission lines for seriescompensation to improve the power-handling capabilities. Shuntcapacitors can be applied to an electrical system for multiple tasks inone single application. More so, in series capacitors, reactive powergeneration is proportional to the square of the load current. In shuntcapacitors, reactive power generation is proportional to the square ofthe voltage.

The matching network 134 may include input sensor 222 and output sensor224. The input sensor 222 may determine the impedance associated withthe matching network 134. The output sensor 224 may determine theimpedance of the RF signal at the processing chamber 136.

The matching network 126 can be associated with a first impedance 212(also referred to as “Z1”) and a second impedance 214 (also referred toas “Z2”). The first impedance 212 can be associated with the firstcapacitor 226, resistive elements 234 (also referred to as “R1”) andinductance elements 232 (also referred to as “L1”) of the parallel pathin the matching network 134. The first impedance 212 can be expressed byEquation 1, seen below, where the term j represents the imaginary unitand the term w represents the angular frequency and is expressed as2*ef, where f is the frequency of the RF signal (e.g., 2.2 MHz, 13.56MHz, 100 MHz, etc.):

$\begin{matrix}{{Z1} = {{R1} + {L\; 1*w*j} + \frac{1}{C1*w*j}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

The second impedance 214 can be associated with the second capacitor228. In some embodiments, the second impedance 214 is also associatedwith one or more passive devices (e.g., inductors, resistors, etc.). Thesecond impedance 214 can be expressed by Equation 2, seen below:

$\begin{matrix}{{Z\; 2} = \frac{1}{C2*w*j}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

The processing chamber 136 can be associated with a third impedance 216(also referred to as “Z3”), associated with impedance from processingchamber components, transition line impedance, feeding rod impedance(from the matching network 134 to the process chamber 136), etc. By wayof example, Z3 can be expressed using the real parts Rp 238 and theimaginary Lp 236 parts of the impedance associated with the processingchamber 136. The Rp and Lp may be measured, in real-time, using theinput impedance sensor 224. The third impedance 216 can be expressed byEquation 3, seen below:

Z3=Rp+Lp*w*j  Equation 3

Accordingly, the total load impedance of the manufacturing equipment 130can be expressed by Equation 4, see below:

$\begin{matrix}{Z_{Total} = {\frac{Z1*( {{Z2} + {Z3}} )}{{Z1} + {Z2} + {Z3}} = \frac{\begin{matrix}{( {{R1} + {L1*w*j} + \frac{1}{C\; 1*w*j}} )*} \\( {{Rp} + {Lp*w*j} + \frac{1}{c2*W*j}} )\end{matrix}}{\begin{matrix}{{R\; 1} + {Rp} + {( {{Lp} + {L\; 1}} )*w*j} +} \\{\frac{1}{C1*w*j} + \frac{1}{C\; 2*w*j}}\end{matrix}}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

FIG. 3 is a flow diagram of a method 300 for generating predictive datato cause a corrective action, according to certain embodiments. Method300 may be performed by processing logic that may include hardware(e.g., circuitry, dedicated logic, programmable logic, microcode,processing device, etc.), software (such as instructions run on aprocessing device, a general purpose computer system, or a dedicatedmachine), firmware, microcode, or a combination thereof. In someembodiment, method 300 may be performed, in part, by simulation system110. In some embodiments, a non-transitory storage medium storesinstructions that when executed by a processing device (e.g., ofsimulation system 110, of server machine 180, of predictive server 112,etc.) cause the processing device to perform of method 300.

For simplicity of explanation, method 300 is depicted and described as aseries of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently and withother operations not presented and described herein. Furthermore, notall illustrated operations may be performed to implement method 300 inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that method 300 couldalternatively be represented as a series of interrelated states via astate diagram or events.

At block 310, the processing logic, implementing method 300, cause theRF generator 132 to generate an RF signal to energize the processingchamber.

At block 312, the processing logic updates the impedance values in thedigital replica. In particular, the processing logic may update theimpedance associated with the matching network model 172 based on thecurrent trace data generated from the input sensor 222, and update theimpedance associated with the processing chamber model 174 based oncurrent trace data 154 generated from the output sensor 224.

At block 314, the processing logic may determine whether the digitalreplica satisfies an accuracy threshold criterion based on the currentimpedance values. For example, the processing logic may use a costfunction formula (e.g., mean difference squared), to determine whetherthe matching network model 172 and the processing chamber model 174 arewithin an allowable margin of error.

By way of example, the processing logic may utilize Equation 5,expressed below, where the term J1 represents the allowable margin oferror, the term Z_(input) represents the impedance determined by theinput sensor 222, and the term angle represents the frequency and phaseof the RF signal:

J1=|Z _(input) −Z _(Total)|²+(angle(Z _(input) −Z _(Total)))²   Equation5

It is understood that the closer the term J1 is to 0, the more accuratethe matching network model 172 is to the parameters of the physicalelements and the dynamics of the matching network 134. Terms associatedwith Equation 5 may be subjected to constraints. In some embodiments,the values of the first capacitor 226 and the second capacitor 228 canbe subject to upper limits (C1 _(max) and C2 _(max)) and lower limits(C1 _(min) and C2 _(min)), expressed as follows: C1 _(min)<C1<C1 _(max);C2 _(min)<C2<C2 _(max). Responsive to the digital replica satisfying thethreshold criterion (e.g., the matching network model 172 and/or theprocessing chamber model 174 each being within an allowable margin oferror), the processing logic proceeds to block 318. Responsive to thedigital replica failing to satisfy the threshold criterion, theprocessing logic proceeds to block 316.

At block 316, the processing logic optimizes the digital replica. In anembodiment, using Equation 5, the processing logic may determine optimalvalues of the resistive elements R1 and/or the inductance elements L1used in the matching network model 172. For example, the processinglogic may update the matching network model 172 by adjusting one or moreparameters (e.g., L1, R1, C1, C2, etc.) until the term J1 satisfies theaccuracy threshold criterion. In an embodiment, the processing logic mayupdate the processing chamber model 174 by adjusting one or moreparameters (e.g., L_(p), R_(p), etc.) using an updating algorithm. Theadjusted parameters may be stored as simulation data 167.

At block 318, the processing logic obtains, from the simulation system110, one or more outputs indicative of predictive data. For example, theprocessing logic may determine optimal values for the first capacitor226 and the second capacitor 228 to minimize the reflected RF signal. Insome embodiments, the processing logic may use the predictive component114 to perform a closed loop simulation using simulation data 167 and amodel 190. The predictive component 114 may generate one or moreoptimization profiles, which may indicate values to which to adjust thefirst capacitor 226 and the second capacitor 228 in order to minimizethe reflected RF signal. In some embodiment, the predictive component114 may further utilize a tuning time parameter in order to minimize thetime necessary to reach the optimal values.

By way of example, processing logic may utilize Equation 6, expressedbelow, to generate the optimization profile, where:

$\begin{matrix}{{J\; 2} = {{w_{1}{\int{\frac{Z - {Z\; 0}}{Z + {Z\; 0}}}}} + {w_{2}*T_{tune}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

The term J2 represents the cost function and may be set to the totalimpedance of the manufacturing equipment 130 (e.g., Z_(total)), the termw₁ represents the weight coefficient associated with the reflectioncoefficient

$( {\frac{Z - {Z\; 0}}{Z + {Z\; 0}}} $

or γ (gamma)), the term T_(tune) is the tuning time, the term w₂ is theweight coefficient associated with the turning time, and the term Z0represents the target impedance (e.g., impedance of the RF generator132, such as 50 ohms). In some embodiments, Equation 6 is subject toconstraints, expressed as follows:

$\begin{matrix}{{{{C\; 1_{\min}} < {C\; 1} < {C\; 1_{\max}}};}{\frac{{dC}\; 1}{dt} < {V_{C\; 1\max}( {C\; 1\mspace{14mu}{velocity}\mspace{14mu}{limit}} )}}} & {{Constraint}\mspace{14mu} 1} \\{{{{C\; 2_{\min}} < {C\; 2} < {C\; 2_{\max}}};}{\frac{{dC}\; 2}{dt} < {V_{C\; 2\max}( {C\; 2\mspace{14mu}{velocity}\mspace{14mu}{limit}} )}}} & {{Constraint}\mspace{14mu} 2} \\{{{T_{tune}\mspace{14mu}{is}\mspace{14mu}{definied}\mspace{14mu}{as}\mspace{14mu}\gamma} < 0.05},{{where}\mspace{14mu}{\frac{Z - {Z\; 0}}{Z + {Z\; 0}}}}} & {{Constraint}\mspace{14mu} 3}\end{matrix}$

The predictive server may use Equation 6 to generate one or moreoptimization profiles for the first capacitor 226 and the secondcapacitor 228. The optimization profiles may indicate reflectivecoefficient values for each tuning combination of the first capacitor226 and the second capacitor 228. The optimization profiles may furtherindicate tuning path to reach capacitor target values. The capacitortarget values may be setting parameters for the first capacitor 226 andthe second capacitor 228 that have the lowest achievable reflectioncoefficient.

FIGS. 4A-C are graphs illustrating example optimization profiles,according to certain embodiments. Specifically, FIG. 4A illustrates areflection coefficient (γ) value for each initial tune value (e.g., from0 to 100%) of the first capacitor 226 and the second capacitor 228.Point 410 indicates the lowest achievable reflection coefficient of theoptimization profile, which is associated with the first capacitor 226tuned to approximately 86% and the second capacitor tuned toapproximately 30% (capacitor target values).

FIG. 4B illustrates the duration required to reach the capacitor targetvalues for each set of initial values for the first capacitor 226 andthe second capacitor 228. For example, if the first capacitor 226 has atune value of 50% and the second capacitor 228 has a tune value of 0%(e.g., point 430), then the corrective action component 122 requires1.25 second to tune the first capacitor 226 and the second capacitor 228to the capacitor target values (e.g., 86% and 30% respectively).

FIG. 4C illustrates a gradient-based match tuning optimization profile.The gradient-based match tuning optimization profile may be defined bythe following cost function:

$\begin{matrix}{J = {{\frac{1}{2}\lbrack ( \frac{Z - {50}}{Z + {50}} ) \rbrack}^{2} + {\frac{1}{2}\lbrack ( \frac{Z - {50}}{Z + {50}} ) \rbrack}^{2}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

By taking partial derivatives of the cost function in Equation 7,control parameters for the optimization model can be determined. Forexample, the following equations show the partial derivatives of thecost function:

$\begin{matrix}{\frac{\partial J}{{\partial C}\; 1} = {f\; 1( {{C\; 1},{C\; 2},{Lp},{Rp}\;,{L\; 1},w} )}} & {{Equation}\mspace{20mu} 8} \\{\frac{\partial J}{{\partial C}\; 2} = {f\; 2( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

The partial derivatives may lead to gradient control values that dictatethe gradient tuning. For example, the gradient control law may lead tothe following gradient controls:

$\begin{matrix}{\frac{{dC}\; 1}{dt} = \{ \begin{matrix}{g_{1},} & {{{if}\mspace{14mu} f\; 1( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )} < {{- c}\; 1}} \\{k_{1}*\lbrack {50 - {{abs}(Z)}} \rbrack} & {{{if}\mspace{14mu}{{f\; 1( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )}}} < {c\; 1}} \\{{- g_{1}},} & {{{if}\mspace{14mu} f\; 1( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )} > {c\; 1}}\end{matrix} } & {{Equation}\mspace{14mu} 10} \\{\frac{{dC}\; 2}{dt} = \{ \begin{matrix}{g_{2},} & {{{if}\mspace{14mu} f\; 2( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )} < {{- c}\; 2}} \\{{- k_{2}}*{{angle}(Z)}} & {{{if}\mspace{14mu}{{f\; 1( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )}}} < {c\; 2}} \\{{- g_{2}},} & {{{if}\mspace{14mu} f\; 1( {{C\; 1},{C\; 2},{Lp},{Rp},{L\; 1},w} )} > {c2}}\end{matrix} } & {{Equation}\mspace{14mu} 11}\end{matrix}$

The positive parameters k₁, k₂, g₁, and g₂ are controller gains. Thevalues of c1 and c2 are the threshold of switching from gradient controlto linear control. For example the switching between gradient controland linear control may be used to remove or otherwise mitigatechattering or noise during a steady state portion of the optimizationprofile.

In some embodiments, the optimization profile may be based on aJacobian-based RF match tuning algorithm. In the context of controllingthe RF match, a Jacobian can predict abrupt changes in the capacitance(e.g. first capacitor C1 226 and second capacitor C2 228 of FIG. 2)caused by past and future iterations of the optimization profile. Asnoted previously, a matching network (e.g., matching network 134 ofFIG. 1) may operate to minimize reflected RF energy by matching theimpedance of the plasma used in the etching process to the impedance ofthe RF signal. For example, a desired real value impedance (e.g., 50Ohms, 75 Ohms, etc.) may matched by the plasma. Using equation 4, theinput impedance may expressed as the following:

$\begin{matrix}{Z = {\frac{Z_{1}( {Z_{2} + Z_{3}} )}{Z_{1} + Z_{2} + Z_{3}} = {Z_{r} + {jZ_{i}}}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$

From Equation 12, a Jacobian matrix can be calculated by the following:

$\begin{matrix}{J_{c} = \begin{bmatrix}\frac{\partial{Z}}{\partial C_{1}} & \frac{{\partial a}\;\tan\; 2( {Z_{r},Z_{i}} )}{\partial C_{1}} \\\frac{\partial{Z}}{\partial C_{3}} & \frac{{\partial a}\;\tan\; 2( {Z_{r},Z_{i}} )}{\partial C_{2}}\end{bmatrix}} & {{Equation}\mspace{14mu} 13}\end{matrix}$

In some embodiments, a Jacobian controller can used as defined by thefollowing:

C(t)=∫₀ ^(t) J _(c) ⁻¹ Ke(t)   Equation 14

where C(t)=[C1 C2], e(t)=[50−abs(Z); −angle(Z)], and K is positivedefinite gain matrix (e.g., Equation 13). In a further embodiments, theJacobian, J_(c), may be modified to adjust the loop gains of amagnitude-phase control system.

In a further embodiment, a damping constant, λ, may be used to prevent asingularity in the inverse calculation of the Jacobian matrix, J_(c),and further cause determining the inverse Jacobian matrix more tractableby the processing logic. In another embodiment, non-direct matrixsolvers can be used to calculate and update tuning values withoutsolving for the inverse Jacobian matrix directly. For example, methodssuch as a matrix least squares solution, iterative solvers, and matrixdecomposition solvers (e.g., upper and lower triangular solvers,singular value decomposition, and the like) can be used, as will bediscussed further in other embodiments.

In some embodiments decentralized control of the optimization profilecan be utilized through Singular Value Decomposition (SVD). For example,an SVD of the Jacobian matrix can be expressed as the following:

J _(c) =UDV ^(T)   Equation 15

U and V are orthogonal matrices and D is a diagonal matrix. For example,D only contains non-zero values along the diagonal elements (e.g.σ_(i)=d_(i,j), along the diagonal) of the matrix. In the absence of asingularity, the diagonal elements decrease down the row and across thecolumns of the matrix (e.g., σ₁≥σ₂ . . . >0). As a result of having Dset as a diagonal matrix, the control of the optimization model isdecentralized. For example, the magnitude control loop and phase controlloop of the impedance can be tuned independently by adjusting one ormore gains of the diagonal matrix D.

In some embodiments, the RF match circuit optimization model may notmeet a threshold precision. For example, the optimization models (e.g.,FIGS. 4A-C) described herein may be dependent on values (e.g.,theoretical or generalized values) that misrepresent uniquecharacteristics of a specific RF match circuit. In some embodiments, thefollowing calibration can be used to more accurately measure parametersand values to determine and predict more precise optimization profiles.

Processing logic may follow a calibration procedure to updateoptimization parameters (e.g., to improve accuracy and precision of theoptimization profile). Processing logic may use an RF match auto-tunemode to find tune position of a first capacitor (e.g., a shunt capacitorC1) and a second capacitor (e.g., a series capacitor C2). The processinglogic can measure the Jacobian using perturbation calculations of thetuned impedance position. For example, processing logic may move C1/C2to the matched positions (e.g., by setting the RF match to a manualcontrol). While keeping C2 unchanged, processing logic may calculate animpedance across a C1 delta (e.g., δ_(c) ₁ =0.5). In this example,processing logic moves C1 to C1+δ_(c) ₁ and reads the input impedence,R₁, for a short duration (e.g., 10 seconds). Processing logic may moveC1 to C1−δ_(c) ₁ and read match input impedance, R₂, for a shortduration (e.g., 10 seconds). While keeping C1 unchanged, processinglogic may calculate an impedance across a C2 delta (e.g., δ_(c) ₂ =0.5).In this example, processing logic moves C2 to C2+δ_(c) ₂ and reads theinput impedance, R₃, for a short duration (e.g., 10 seconds). Processinglogic may move C2 to C2−δ_(c) ₂ and read match input impedance, R₄, fora short duration (e.g, 10 seconds). The processing logic may thencalculate the Jacobian using the following equations and parametervalues as described in this calibration example.

$\begin{matrix}{{\delta_{c_{1\mspace{11mu}{mag}}} = \frac{{{abs}( R_{1} )} - {{abs}( R_{2} )}}{2*\delta_{C_{1}}}}{\delta_{c_{1\mspace{11mu}{phase}}} = \frac{{{angle}( R_{1} )} - {{angle}( R_{2} )}}{2*\delta_{C_{1}}}}{\delta_{c_{2\mspace{11mu}{mag}}} = \frac{{{abs}( R_{3} )} - {{abs}( R_{4} )}}{2*\delta_{C_{2}}}}{\delta_{c_{2\mspace{11mu}{phase}}} = \frac{{{angle}( R_{3} )} - {{angle}( R_{4} )}}{2*\delta_{C_{2}}}}{J_{c} = \lbrack {\delta_{C_{2\mspace{11mu}{mag}}},\delta_{c_{1\mspace{11mu}{phase}}},\delta_{c_{2\mspace{11mu}{mag}}},\delta_{c_{2\mspace{11mu}{phase}}}} \rbrack}} & {{Equations}\mspace{14mu} 16\text{-}20}\end{matrix}$

The Jacobian identified in Equation 20 may then be used in associationwith other optimization profiles (e.g., SVD, damping, etc.), asdiscussed herein.

In some embodiments, the processing logic may continue generatingpredictive data (e.g., optimization profiles) until said data satisfiesa threshold criterion based on the weighing coefficients. In particular,the weighting coefficients may be set to 1 (e.g., w₁+w₂=1 or 100%). Auser may alter the weighing coefficients based on a preference forminimal reflective power against tuning time. For example, if the userprioritizes minimizing match tuning time, the user may set w₂ to a highvalue. If the user prioritizes minimizing reflected power, the user mayset w₁ to a high value.

At block 320, the processing logic causes, based on the predictive data,performance of a corrective action. For example, the processing logiccan select a tuning path from the optimization profile. The controller138 may adjust the first capacitor 226 and/or the second capacitor 228based on the tuning path. FIG. 5 is a graph illustrating multiple tuningpaths associated with the first capacitor 226 and/or the secondcapacitor 228. In particular, FIG. 5 shows tuning path towards thecapacitor target values for each initial tune value (e.g., from 0 to100%) of the first capacitor 226 and the second capacitor 228.

FIG. 6 is a block diagram illustrating a computer system 600, accordingto certain embodiments. In some embodiments, computer system 600 may beconnected (e.g., via a network, such as a Local Area Network (LAN), anintranet, an extranet, or the Internet) to other computer systems.Computer system 600 may operate in the capacity of a server or a clientcomputer in a client-server environment, or as a peer computer in apeer-to-peer or distributed network environment. Computer system 600 maybe provided by a personal computer (PC), a tablet PC, a Set-Top Box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, switch or bridge, or any devicecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that device. Further, the term“computer” shall include any collection of computers that individuallyor jointly execute a set (or multiple sets) of instructions to performany one or more of the methods described herein.

In a further aspect, the computer system 600 may include a processingdevice 602, a volatile memory 604 (e.g., Random Access Memory (RAM)), anon-volatile memory 606 (e.g., Read-Only Memory (ROM) orElectrically-Erasable Programmable ROM (EEPROM)), and a data storagedevice 616, which may communicate with each other via a bus 608.

Processing device 602 may be provided by one or more processors such asa general purpose processor (such as, for example, a Complex InstructionSet Computing (CISC) microprocessor, a Reduced Instruction Set Computing(RISC) microprocessor, a Very Long Instruction Word (VLIW)microprocessor, a microprocessor implementing other types of instructionsets, or a microprocessor implementing a combination of types ofinstruction sets) or a specialized processor (such as, for example, anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a Digital Signal Processor (DSP), or a networkprocessor).

Computer system 600 may further include a network interface device 622(e.g., coupled to network 674). Computer system 600 also may include avideo display unit 610 (e.g., an LCD), an alphanumeric input device 612(e.g., a keyboard), a cursor control device 614 (e.g., a mouse), and asignal generation device 620.

In some implementations, data storage device 616 may include anon-transitory computer-readable storage medium 624 on which may storeinstructions 626 encoding any one or more of the methods or functionsdescribed herein, including instructions encoding components of FIG. 1(e.g., corrective action component 122, predictive component 114, etc.)and for implementing methods described herein.

Instructions 626 may also reside, completely or partially, withinvolatile memory 604 and/or within processing device 602 during executionthereof by computer system 600, hence, volatile memory 604 andprocessing device 602 may also constitute machine-readable storagemedia.

While computer-readable storage medium 624 is shown in the illustrativeexamples as a single medium, the term “computer-readable storage medium”shall include a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of executable instructions. The term“computer-readable storage medium” shall also include any tangiblemedium that is capable of storing or encoding a set of instructions forexecution by a computer that cause the computer to perform any one ormore of the methods described herein. The term “computer-readablestorage medium” shall include, but not be limited to, solid-statememories, optical media, and magnetic media.

The methods, components, and features described herein may beimplemented by discrete hardware components or may be integrated in thefunctionality of other hardware components such as ASICS, FPGAs, DSPs orsimilar devices. In addition, the methods, components, and features maybe implemented by firmware modules or functional circuitry withinhardware devices. Further, the methods, components, and features may beimplemented in any combination of hardware devices and computer programcomponents, or in computer programs.

Unless specifically stated otherwise, terms such as “receiving,”“performing,” “providing,” “obtaining,” “causing,” “accessing,”“determining,” “adding,” “using,” “training,” or the like, refer toactions and processes performed or implemented by computer systems thatmanipulates and transforms data represented as physical (electronic)quantities within the computer system registers and memories into otherdata similarly represented as physical quantities within the computersystem memories or registers or other such information storage,transmission or display devices. Also, the terms “first,” “second,”“third,” “fourth,” etc. as used herein are meant as labels todistinguish among different elements and may not have an ordinal meaningaccording to their numerical designation.

Examples described herein also relate to an apparatus for performing themethods described herein. This apparatus may be specially constructedfor performing the methods described herein, or it may include a generalpurpose computer system selectively programmed by a computer programstored in the computer system. Such a computer program may be stored ina computer-readable tangible storage medium.

The methods and illustrative examples described herein are notinherently related to any particular computer or other apparatus.Various general purpose systems may be used in accordance with theteachings described herein, or it may prove convenient to construct morespecialized apparatus to perform methods described herein and/or each oftheir individual functions, routines, subroutines, or operations.Examples of the structure for a variety of these systems are set forthin the description above.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples and implementations, itwill be recognized that the present disclosure is not limited to theexamples and implementations described. The scope of the disclosureshould be determined with reference to the following claims, along withthe full scope of equivalents to which the claims are entitled.

1. A method comprising: causing manufacturing equipment to generate a RFsignal to energize a processing chamber associated with themanufacturing equipment; receiving, from one or more sensors associatedwith the manufacturing equipment, current trace data associated the RFsignal; updating impedance values of a digital replica associated withthe manufacturing equipment based on the current trace data; obtaining,from the digital replica, one or more outputs indicative of predictivedata; and causing, based on the predictive data, performance of one ormore corrective actions associated with the manufacturing equipment. 2.The method of claim 1, further comprising: responsive to determiningthat digital replica fails to satisfy an accuracy threshold criterionbased on the current trace data, performing an optimization of thedigital replica.
 3. The method of claim 1, wherein updating theimpedance values of the digital replica comprises: updating a processingchamber model associated with the digital replica using trace data froman output impedance sensor, wherein the output impedance sensor isassociated with a matching network of the manufacturing equipment. 4.The method of claim 1, wherein updating the impedance values of thedigital replica comprises: updating a matching network model associatedwith the digital replica using trace data from an input impedance sensorand an output impedance sensor, wherein the input impedance sensor andthe output impedance sensor are associated with a matching network ofthe manufacturing equipment.
 5. The method of claim 1, wherein the oneor more outputs indicative of predictive data are generated using atrained machine learning model.
 6. The method of claim 1, wherein thepredictive data comprises one or more tune settings for one or morevariable capacitors associated with a matching network of themanufacturing equipment.
 7. The method of claim 1, wherein thecorrective action comprises adjusting one or more variable capacitorsassociated with a matching network of the manufacturing equipment basedon the predictive data.
 8. A system comprising: a memory; and aprocessing device, coupled to the memory, to: cause manufacturingequipment to generate a RF signal to energize a processing chamberassociated with the manufacturing equipment; receive, from one or moresensors associated with the manufacturing equipment, current trace dataassociated the RF signal; update impedance values of a digital replicaassociated with the manufacturing equipment based on the current tracedata; obtain, from the digital replica, one or more outputs indicativeof predictive data; and cause, based on the predictive data, performanceof one or more corrective actions associated with the manufacturingequipment.
 9. The system of claim 8, wherein the processing device isfurther to: responsive to determining that digital replica fails tosatisfy an accuracy threshold criterion based on the current trace data,perform an optimization of the digital replica.
 10. The system of claim8, wherein to update the impedance values of the digital replica, theprocessing device is further to: update a processing chamber modelassociated with the digital replica using trace data from an outputimpedance sensor, wherein the output impedance sensor is associated witha matching network of the manufacturing equipment.
 11. The system ofclaim 8, wherein to update the impedance values of the digital replica,the processing device is further to: update a matching network modelassociated with the digital replica using trace data from an inputimpedance sensor and an output impedance sensor, wherein the inputimpedance sensor and the output impedance sensor are associated with amatching network of the manufacturing equipment.
 12. The system of claim8, wherein the one or more outputs indicative of predictive data aregenerated using a trained machine learning model.
 13. The system ofclaim 8, wherein the predictive data comprises one or more tune settingsfor one or more variable capacitors associated with a matching networkof the manufacturing equipment.
 14. The system of claim 8, wherein thecorrective action comprises adjusting one or more variable capacitorsassociated with a matching network of the manufacturing equipment basedon the predictive data
 15. A non-transitory machine-readable storagemedium storing instructions which, when executed cause a processingdevice to perform operations comprising: causing manufacturing equipmentto generate a RF signal to energize a processing chamber associated withthe manufacturing equipment; receiving, from one or more sensorsassociated with the manufacturing equipment, current trace dataassociated the RF signal; updating impedance values of a digital replicaassociated with the manufacturing equipment based on the current tracedata; obtaining, from the digital replica, one or more outputsindicative of predictive data; and causing, based on the predictivedata, performance of one or more corrective actions associated with themanufacturing equipment.
 16. The non-transitory machine-readable storagemedium of claim 15, wherein the operations further comprise: responsiveto determining that digital replica fails to satisfy an accuracythreshold criterion based on the current trace data, performing anoptimization of the digital replica.
 17. The non-transitorymachine-readable storage medium of claim 15, wherein updating theimpedance values of the digital replica comprises: updating a processingchamber model associated with the digital replica using trace data froman output impedance sensor, wherein the output impedance sensor isassociated with a matching network of the manufacturing equipment. 18.The non-transitory machine-readable storage medium of claim 15, whereinupdating the impedance values of the digital replica comprises: updatinga matching network model associated with the digital replica using tracedata from an input impedance sensor and an output impedance sensor,wherein the input impedance sensor and the output impedance sensor areassociated with a matching network of the manufacturing equipment. 19.The non-transitory machine-readable storage medium of claim 15, whereinthe one or more outputs indicative of predictive data are generatedusing a trained machine learning model.
 20. The non-transitorymachine-readable storage medium of claim 15, wherein the predictive datacomprises one or more tune settings for one or more variable capacitorsassociated with a matching network of the manufacturing equipment, andwherein the corrective action comprises adjusting one or more variablecapacitors associated with a matching network of the manufacturingequipment based on the predictive data.